Atmospheric variability¶
The ERA5 reanalysis dataset provides information about the atmosphere that can help contextualize sea ice conditions. Here, we’ll plot 2 meter temperature and downwelling longwave radiation from ERA5 to get a sense for the atmospheric conditions during each winter season.
import xarray as xr # For working with gridded climate data
from utils.read_data_utils import read_book_data # Helper function for reading the data from the bucket
from utils.plotting_utils import static_winter_comparison_lineplot, staticArcticMaps, interactiveArcticMaps, compute_gridcell_winter_means # Plotting utils
# Plotting dependencies
%config InlineBackend.figure_format = 'retina'
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 150 # Sets figure size in the notebook
# Remove warnings to improve display
import warnings
warnings.filterwarnings('ignore')
Read in the data¶
book_ds = read_book_data() # Read/download the data
book_ds = book_ds.where(book_ds.region_mask.isin([1,2,3,4,5,6])) # Restrict to the inner arctic
years = [2018,2019,2020] # Years over which to perform analysis
save_label='Inner_Arctic_MYI'
# Uncomment out to set an additional ice type mask too and change the save_label accordingly (0 = FYI, 1 = MYI)
book_ds = book_ds.where(book_ds.ice_type==1)
Map monthly data¶
Here, we’ll use the interactiveArcticMaps function to display the data. You can change the variable to display by changing data_var in the code cell below if you run the notebook in Binder.
data_var = "t2m"
interactiveArcticMaps(book_ds[data_var], cmap="coolwarm", frame_width=500)